You choose a topic Case Study Example | Topics and Well Written Essays - 750 words - 1

You choose a topic - Case Study Example High cost of drug abuse in work place is also a justification for workplace drug testing. The propensity of drug abuse in work place increases randomly the operational cost in an organization. Drug abuse in workplace reduces employees’ productivity, it also increases the insurance premium, and it as well leads to the increase in the number of employees who are fired or die as a result of drug abuse. Drug abuse in workplace also increases the administrative cost; it leads to decline in employees’ morale, increased theft cases, damage of properties in workplace as well as tarnishing the company public image. All this negative effect affects unconstructively the organization performance. To maintain the required performance in an organization, frequent workplace drug test is extremely critical. Workplace drug testing is also justified by increased conspicuous accidents resulting from drug abuse. The 1982 accident on the USS Nimitz was as a result of workplace drug abuse. Other accidents that are related to workplace drug abuse also include the 1987 locomotive accident which caused death of sixteen people while other 176 were injured. In 1989 large oil tanker spilled million of crude oil into water bodies in Alaska. The split led to the loss of animal life as well as the destruction of social and economic structure in the area. All this accident was a result of drug abuse in workplace. These accidents led to the introduction of omnibus transportation employees Act of 1991. The Act required alcohol and drug testing to all transport employees (William, 2002) The workplace drug testing has been blamed vehemently for violating employees civil rights. Drug testing especially urine test is seriously condemned for violating individual privacy rights. To avoid false positive results, employees are requested to state all the over –the- counter drugs and other prescriptions used within the last thirty days. This requirement exposes

Impact of Changes on Target Companys Structure Assignment

Impact of Changes on Target Companys Structure - Assignment Example Jones postulates that the organizational structures are two in kind, namely: mechanistic structure and organic structure (209). Wei, Liu and Harden postulate that the degree to this subdivision of jobs is the work specialization in the organization (19). Securing and Goldbach refers to the term centralized as â€Å"the concentration of authority and responsibility for decision making in the hands of managers at the top of an organization’s hierarchy† (214). In other words, Kumar and Bhat describe the term centralization in an organization as â€Å"the degree to which decision making is concentrated in a single point† (346). The degree of decision making concentrated in numerous points shows decentralization in an organization (Applebaum & et.al., 2008). The Target Company has a mechanistic organizational structure. It has a high level of specialization which allows it to divide the jobs into significant groups. The work specialization requires that each person should be placed according to his/her qualifications and experience to ensure a maximum level of specialization in their work. Each department has been allotted different work that is not given to the other department in the organization (Ouchi, 1977). The answer to this question is that all stores of the company are centrally controlled through a centrally located center for every region. Target Company has a mechanistic structure that allows it to have a centrally located center that has the highest degree of decision-making power to control all the stores located within its jurisdiction. The answer to this question is that the change in the company structure through the incorporation of a grocer sector in Target Company will positively impact its vertical and horizontal aspects. Previously, the Target Company had a focused company strategy to have 5 self-managed teams (an i.e. horizontal aspect of the company structure). Furthermore, the horizontal aspect of the company structure  included decision making through consensus and voting. Dries and Swinnen argued that vertical and horizontal spillover effects produce improved quality of product and significant growth of small and medium suppliers (1525).  

The College Experience Essay Example for Free

The College Experience Essay The years spent in college are a big transitional stage in every ones life. The years in college are spent in preparation for future endeavors in the real world. Choosing a college is imperative in that it can potentially effect your level of preparation for a professional career or in some cases graduate study. Your choice will impact the quality of education you will be receiving as well as shape your independence. When it came time for me to begin looking at schools I wasnt sure where I wanted to go. I felt that their were so many choices and I found it difficult narrowing my choices down. Factors such as the schools size, the schools locale, and the cost were all part of my decision process. Up until this point schools such as Xavier University of Louisiana, Hampton University, North Carolina State University, Howard University, and North Carolina Central University were all realistic choices for school. North Carolina Central became my top choice by virtue of its close proximity to my hometown Raleigh, the cost of attendance, and the predominately black enrollment . The location was important because I knew that I would be surrounded with people who shared similar experiences and back grounds while at the same time I would be exposed to students with different experiences and back grounds from that of my own. I felt that North Carolina Centrals location would make it easier adjusting to college simply because being a citizen of Raleigh I am naturally familiar with the area. The size of the institution was also a big factor in my choosing North Carolina Central. I felt that in large schools there was little student teacher interaction, in or out of the classroom but in a smaller school like NCCU I would have an easier time developing a relationship with my professor. In retrospect I feel that this hold true because of teacher to student ratio encourages a more intimate learning environment. When I arrived at Central in January I was surprised by the familiar faces I saw. In my first couple weeks I got reacquainted with many former friends from high school and various summer programs I had participated in. Having acquaintances prior to my attending school made my transition considerably smoother. Having a friend that was familiar with my surroundings was beneficial to me. Living with roommates was an adjustment because I am the only child I never had to really share my space with anyone. The independence and responsibility that comes with living in dorm benefited me and helped me adapt to the new environment and new people. By virtue of my experiences both academically and socially I truly feel that I am in an institution that embraces people like myself and I am anxious to create a legacy of my own to add to the rich tradition here at North Carolina Central University. Overall I am completely satisfied with choosing North Carolina Central University as my school because I researched schools and made the decision best for me. I knew that my choosing Central was an important step in my life and my actions that followed would have to reflect my goal of graduating.

Video Game To Movie Cross Media Film Studies Essay

Video Game To Movie Cross Media Film Studies Essay Following the Teaser Show the benefits, its about selling your service. If the service allows them to win money this should be clear, if its of a narrative experience then the teaser, should have that inherent embedded into it. Reeling Them In- You need to constantly appeal to your audience and give a sense that your service is worth spending more time in. This is where meticulous planning of phased releases of story fragments across the media channels comes into its own. To some extent this is no different from a series editor/writer who has to arc each episodes narrative to keep them coming back for more. 2.2.4 Breaking the Fourth Wall Usually seen as an ad or end credit sequence where the voice over tells you why it is worth your while to carry on somewhere else with them. Breaks the fourth wall but is a clear directive. 2.2.5 Parallel Dimension There is something on another platform running synchronous to the one you are watching. Such as watching football on TV, while listening to Radio 5, because I prefer a more in-depth commentary. Now of course there are many other parallel channels. Web, mobile and TV all running along with each other. I am more and more involved with parallels between real and virtual worlds. The techniques to draw audiences into these experiences are often inherent in the service such that if you are on one you can actually see the other one taking place. 2.2.6 On Their Own The expectation from the audience that there will be other media elsewhere drives their journey through the story. The technique here is the hardest to identify but it follows the same technique of designing a physical hunt. You hide things, dont give easy clues and then set the ground rules. They expect something on all platforms and this is where the term cross-media will eventually become redundant. All properties will have something on all platforms, the same way DVDs now all have extras. If they have enjoyed cross-media experiences from you before they will come back. It is about trust, being consistent and giving them a media world to play in. 2.3 Franchise A franchise is a property which involves characters, settings and trademarks of original media. This can be a film, a piece of literature a TV program or a video game. Generally, a whole series is made in a particular medium, and multiple sequels are often planned well in advance. The most famous franchise to use Cross-Media Narrative is the Star Wars Saga, since its creation in 1977 it still expands its universe to the present day (2010). It consists of 6 feature films, and has since been extended, to more films, radio films, books, comic books, Animated TV series and also Video Games. Star Wars is an epic science fiction franchise conceived by George Lucas. It is best known for its 6 feature films, this consisted of two trilogies, the original trilogy and then the prequel trilogy. The first film in the series was originally released on May 25, 1977, by 20th Century Fox, and it became a worldwide pop culture phenomenon, the film spawned two immediate sequels, released at three-year intervals. Sixteen years after the release of the first trilogys final film, the first in a new prequel trilogy of films was released, again released at three-year intervals, with the final film released on May 19, 2005. Other Media, in which the Star Wars film series has spawned, includes books, a TV series, video games, and comic books. All of this supplements to the film trilogies and comprises the Star Wars Expanded Universe, which is an umbrella term for officially licensed Star Wars material outside of the six feature films and keeps the franchise going in the interim between the film trilogies. In 2008, a 7th film was released to theaters, Star Wars: The Clone Wars and it was the first ever worldwide theatrical Star Wars film outside of the main trilogies. It was the franchises first animated film, and was created as an introduction to the TV series Star Wars: The Clone Wars, a CGI animated series. Since the creation Star Wars in 1977, the franchise keeps on expanding and each type of media created still stays within the Star Wars timeline of events taking place anywhere from 25,000 years before Episode 1: The Phantom Menace to 140 years after Episode 6: Return of the Jedi. George Lucas retains artistic control over the Star Wars universe. For example, the death of central characters and similar changes in the status quo must first pass his screening before authors are given the go-ahead. In addition, Lucasfilm Licensing devotes efforts to ensure continuity between the works of various authors across companies. George Lucas has played a large role in the production of various television projects, usually serving as storywriter or executive producer, as mentioned in Pablo Hidalgos article From EU to Episode II: Aayla Secura. Star Wars has had a number of radio adaptations, such as A New Hope which was first broadcast on National public radio in 1981(StarWars.com 2010) Since the creation Star Wars in 1977, so many different types of media have been created, even now there is currently the idea of a Blu-ray release and 3D release of the trilogies, all of this keeps the Star Wars franchise alive. As you can see the Star Wars franchise is so huge and successful it is a prime example of how cross-media narrative is used and been effective. Other successful franchises are Indiana Jones, Batman, Superman, X-Men and others. Now is this purely for the viewers benefit or is it for financial profit for the studio. This is what I hope to find out. 3 Types of Cross-Media Currently the main types of cross-media are TV shows that have been made into films and also TV shows that have created webisodes. Also I will show films that have spawned video games and video game that have spawned films. I will cover all these topics in this section, giving examples of franchises that have expanded there universe, a quick example being how Batman has gone on to make films, video games, cartoons, TV series, comics etc. 3.1 TV Shows made into Films Now and then certain TV Shows, are that successful that Films are made from the series, a good example of this is Sex and the City, and also the series Police Squad which went on to make the Naked Gun Trilogy. Sex and the City Sex and the City is an American Television series. The original run of the show was broadcast on HBO from 1998 until 2004, for a total of ninety-four episodes. The show was set in New York City, and it focused on four American women, three of them in their mid-thirties and one in her forties. The series had multiple continuing story lines throughout episodes and seasons and it tackled socially relevant issues such as sexually transmitted diseases, safe sex, and promiscuity. It specifically examined the lives of big-city professional women in the late 1990s/early 2000s and how changing roles and expectations for women affected the characters. A feature film based on Sex and the City has been produced. The film originally was slated for production near the end of the broadcast series run in 2004, but the movie deal fell through at that time. The film was later released in 2008 and Michael Patrick King wrote and directed. The four lead actresses returned to reprise their roles, and Chris Noth signed to reprise his role as Mr. Big. The plot of the film revolves around the lives of the four main characters, four years after the time frame of the finale of the series. The films world premiere was in Londons Leicester Square in early May 2008. The film was released on May 28, 2008 in the UK and was released May 30, 2008 in the US with an unprecedented $55.7 million three-day gross. The debut made Sex and the City the top-opening R-rated romantic comedy of all time. Police Squad Police Squad! Was a comedy TV series which was first broadcast in 1982. The show was a spoof of police dramas, packed with visual gags and non sequiturs. Despite critical acclaim, the show was cancelled after just six episodes. However these 6 episodes were enough to gain a strong cult following through repeat broadcasts, and this led to the 1988 film version The Naked Gun: From the Files of Police Squad! And two further sequels. Many gags from the show were recycled for the films (Roger Ebert 1988). The film The Naked Gun: From the Files of Police Squad! Performing well at the box office, grossing around $78,756,177 (Box Office Mojo (n, d)).It became a hit comedy, it became so popular that two sequels were created The Naked Gun 2 ½: The Smell of Fear in 1991 and Naked Gun 33à ¢Ã¢â‚¬ ¦Ã¢â‚¬Å": The Final Insult in 1994. The Naked Gun 2 ½: The Smell of Fear was considered the most successful of the three, grossing around $86,930,411(Box Office Mojo (n, d)), while Naked Gun 33à ¢Ã¢â‚¬ ¦Ã¢â‚¬Å": The Final Insult grossed $51,132,598 (Box Office Mojo (n, d)). Roger Ebert rated the first movie 3 1/2 out of four stars, and gave 3 stars to the two following films. The second film won a Golden Screen award for Best Picture. The creators stated this in a featurette for The Naked Gun 2 ½: The Smell of Fear. 3.2 Films made into Video Games Most films when being created also make video games to accommodate the film. The story is usually similar to the film, we see this in James Camerons Avatar, but this is not always the case as film such as the Indiana Jones franchise, the video games tend to have their own story, just set in the same fictional universe. Avatar James Camerons Avatar is an epic science fiction film. The film is set in the year 2154, when humans are mining a precious mineral called unobtanium on Pandora, a lush moon of gas giant in the Alpha Centauri star system. The expansion of the mining colony threatens the continued existence of a local tribe of Navi, a sentient humanoid species indigenous to Pandora. The films title refers to the genetically engineered Navi-human hybrid bodies used by a team of researchers to interact with the natives of Pandora. Ubisoft Montreal was chosen by James Cameron to create an Avatar game for the film in 2007. The filmmakers and game developers collaborated heavily, and James Cameron decided to include some of Ubisofts vehicle and creature designs into the film. James Camerons Avatar: The Game was released on December 1, 2009, for most home video game consoles. James Camerons Avatar: The Game is 2009 is a 3rd person action video game. Speaking in an interview with MCV, It was announced by Ubisoft that it will be using the same technology as the film to be displayed in stereoscopic 3D (PSU 2009) Indiana Jones Indiana Jones is a fictional character in the Indiana Jones franchise. The characters full name is Dr. Henry Walton Indiana Jones, Jr and first appeared in Raiders of the Lost Ark in 1981, is the first of four films, and pits Indiana Jones against the Nazis, who search for the Ark of the Covenant, in an attempt to make their army invincible. The film was to be followed by Indiana Jones and the Temple of Doom in 1984, Indiana Jones and the Last Crusade in 1989, and Indiana Jones and the Kingdom of the Crystal Skull in 2008. Other than the films the Indiana Jones franchise has a TV series, novels, comics, video games, and other media. The character has appeared in many licensed video games the latest being Indiana Jones and the Staff of Kings, which was released on June 9, 2009 Indiana Jones and the Staff of Kings is the third in the series of original 3D Indiana Jones games, preceded by Indiana Jones and the Emperors Tomb, and Indiana Jones and the Infernal Machine. 3.3 Video Games made into Films When certain video games become increasingly popular there has always been speculation about making that game into a film. This is has happened with many games, such as Tomb Raider, Resident Evil, Hitman, Prince of Persia, and many others. The films may not always be true to the game but are usually a commercial success. One of the latest films that are under speculation to be made into a film is the PlayStation 3 game Uncharted. Tomb Raider Tomb Raider is a video game developed by Core Design and published by Eidos Interactive. Tomb Raider follows the exploits of Lara Croft, an English female archaeologist in search of ancient treasures kind of like Indiana Jones. It was originally released in 1996 for the Sega Saturn followed shortly thereafter for MS-DOS and PlayStation versions. Since the release of the original in 1996, the series developed into a lucrative franchise of related media, and Lara went on to become a major icon of the video game industry. The game was commercially and critically successful, and is considered widely influential according to Gamespots article 15 most influential games of all time Gamespot (n. d.). It spawned a number of sequels and a franchise of related media. Six games in the series were developed by Core Design, and the latest three by Crystal Dynamics. To date two movies, Lara Croft: Tomb Raider and Lara Croft Tomb Raider: The Cradle of Life, have been produced starring American actress Angelina Jolie as Lara Croft. Lara Croft: Tomb Raider is a 2001 adventure film which was an adaptation from the Tomb Raider video game series, as it didnt follow the same story as in the game but instead had an entirely new story. A sequel, Lara Croft Tomb Raider: The Cradle of Life was released in 2003. Hitman Hitman is a stealth game developed by Danish company IO Interactive, now a division of Square Enix. The game series has since expanded into a novel, Hitman: Enemy Within and in 2007 a film was released loosely based on the storyline of the games. IESB has confirmed that 20th Century Fox has hired writer Kyle Ward to pen the script for the sequel to Hitman (IESB 2009) . Adrian Askarieh, Daniel Alter and Chuck Gordon will return as producers. Introducing British actor David Hess and Timothy Olyphant will return as 47 in June of 2010. 3.4 Films made into TV shows When a film has become really popular, it is often made into a TV show, to keep the story and the films universe alive. Star Wars has done this, with creating the animated TV series Star Wars: The Clone Wars. Also Terminator has done this, by creating Terminator: The Sarah Connor Chronicles. Star Wars Star Wars is an epic space opera franchise, best known for its 6 feature films; this consisted of two trilogies, the original trilogy and then the prequel trilogy. In 2008, Star Wars: The Clone Wars was released, and it was the first ever worldwide theatrical Star Wars film outside of the main trilogies. It was the franchises first animated film, and was intended as an introduction to the series Star Wars: The Clone Wars, a 3D CGI animated series based on a previous 2D animated series of a similar name made in 2003. Star Wars: The Clone Wars is a 3D CGI animated television series. It is set in the fictional Star Wars Galaxy, during the same time period as the previous 2003 series. The show itself takes place from 22 BBY (Before the battle of Yavin) to 20 BBY. Each episode has a running time of 22 minutes, to fill a half-hour time slot. Season 2 premiered on October 2, 2009 and now Star Wars: The Clone Wars has been granted a third season, beginning in October 2010. Terminator The Terminator series is a science fiction franchise encompassing a series of films and ancillary media concerning battles between Skynets artificial intelligent machine network, and John Connors Resistance forces and the rest of the human race. The Terminator is the original film which was released in 1984. The film has been followed by three sequels. The franchise has evolved to one of the most successful franchises of all time (IGN 2006), which includes video games and a TV series. From the success of the franchise, a spin-off was created titled Terminator: The Sarah Connor Chronicles with Lena Headey as Sarah Connor and Thomas Dekker as John Connor. The series, created by Josh Friedman, centers on Sarah and John after Terminator 2 as they try to live under the radar after the explosion at Cyberdyne. Summer Glau plays a female Terminator protecting the Connors. The series premiered on Sunday, January 13, 2008. Production for the series was provided by Terminator 2 and Terminator 3 producers and it consisted of 2 seasons. Season one with nine, 40 minute episodes, and Season two with twenty two, 40 minute episodes. The Terminator Franchise has had a measurable impact on popular culture, most notably James Camerons original films, The Terminator and Terminator 2: Judgment Day. The film franchise placed it at 17 on the top 25 greatest film franchises of all time by IGN and is also in the top 25 grossing franchises of all time. 3.5 TV Shows that have created Webisodes Many TV shows have made webisodes lately, these shows include, Heroes, Chuck, and The Office: An America Workplace. Heroes Heroes, is a science fiction TV drama series by Tim Kring that tells the stories of ordinary people who discover extraordinary abilities, and how these abilities take effect in the characters lives. The series emulates the aesthetic style and storytelling of Comic Books, using short, multi-episode story arcs that build upon a larger, more encompassing arc. Since its beginning there have now been 4 seasons and a number of webseries. The first Heroes webseries was released on July 14, 2008 between seasons 2 and 3, called Going Postal. It was a trilogy of online-only videos introducing Echo DeMille, a seemingly ordinary mailman with an extraordinary ability. On November 10, 2008 during Season 3, the second Heroes webseries, Destiny, was released. This webseries is a quadrilogy. A Month after the third Heroes webseries, The Recruit, was released on December 15, 2008. The Recruit introduces Rachel Mills, a marine who survives the explosion at Pinehearst. This follows the finale of volume three. Also in that month the fourth Heroes webseries, Hard Knox, was released. Hard Knox flashes back to 18 months ago, to a time when Matt Parkman knew the villain, Knox, before his abilities began to manifest. Following Hard Knox, Nowhere Man was released in April 2009, and it picked up where the third season leaves off, focusing on the life of Eric Doyle. Then on September 28, 2009, Slow Burn began airing alongside Season 4. It showed behind-the-scenes goings-on of the Sullivan Bros. Carnival as it follows the character of Lydia, revealing she has a pyrokinetic daughter named Amanda who she discovers is in trouble and tries to help. Chuck Chuck is an action-comedy television series created by Josh Schwartz and Chris Fedak. The show centers around the main character Chuck Bartowski an average computer-whiz-next-door, who receives an encoded e-mail from an old college friend now working in the CIA; the message embeds the only remaining copy of the worlds greatest spy secrets into Chucks brain. It currently has 3 seasons, and 3 webseries. It has Chuck Versus the Webisodes, which consists of 5 Buy More training videos. Meet the Nerd Herders, which also contains 5 webisodes, each video contains staff from the Buy More, giving their thoughts on specific topics. The third webseries is Morgans Vlog, which is a video blog, it has 4 parts, but the first 3 parts, are Morgan and Chuck on making a movie end a lot quicker, and the fourth episodes is Morgans view on movie villains. The Office: An American Workplace The Office: An American Workplace is an adaptation of the BBC series, The Office a Mockumentary created by Ricky Gervais and Stephan Merchant, the series depicts the everyday lives of office employees in the Scranton, Pennsylvania, branch of the fictional Dunder Mifflin Paper Company. To simulate the look of an actual documentary, it is filmed in a single-camera setup, without a studio audience or any canned laughter. There has currently been 6 webseries, the first being The Accountants which aired between the second and third seasons. The webisodes follow the accountants Angela, Oscar, and Kevin as they try to find out who stole $3,000 from the books. Between the fourth and fifth seasons, the summer webisode series Kevins Loan was released in four weekly episodes, the first premiering on July 10, 2008. Then during the airing of the fifth season, the winter webisode series The Outburst was released in weekly episodes, the first premiering on November 20, 2008. At the conclusion of the fifth season, the summer webisode series Blackmail was released similarly to the previous two, in weekly episodes. During the sixth season, the webisode series Subtle Sexuality aired in its entirety on October 29, 2009. The series focuses on Kelly and Erin forming their own girl group, called Subtle Sexuality. The first two webisodes document the behind-the-scenes aspects and troubles of shooting the music video for their first single Male Prima Donna, while the third and final webisode is the music video itself, which features Ryan as a guest rapper and Andy singing the bridge. Also during the sixth season, the webisode series The Mentor aired in its entirety on March 4, 2010. Erin wants to be an accountant so Angela decides to train her. But, Erins relationship with Kelly turns bad when she spends too much time with Angela. Kelly and Ryan then interfere in Angela and Erins relationship. 4 Financial Successes After seeing how many games are made into films and films are made into games, I wanted to see if financially it is a success. Is it worth expanding the franchise if the financial profit is not there? 4.1 Game to Film Tomb Raider Lara Croft: Tomb Raider Budget $115,000,000 Gross Revenue $274,703,340 (Box Office Guru 2001) Tomb Raider debuted at number one with $48.2 million, giving Paramount its second-best debut and the fourth-highest debut of 2001. It beat the opening record for a film featuring a female protagonist, and it is currently the most successful video game adaptation to date, grossing $300,000,000 worldwide. Cradle of Life Budget $120,000,000 Gross Revenue $156,505,388 Despite its better review, Cradle of Life suffered a disappointing opening weekend, as it debuted in fourth place with a take of $21.7 million (Box Office Mojo 2003) a 55% drop from the originals opening gross of $47.7 million (Box Office Mojo 2001). The film finished with a domestic gross of only $65 million; therefore it had to rely on the foreign box office to make a profit. Its total earnings amounted to $156.5 million, which represented a loss of $118 million when compared to the success of the original; the loss nearly equals the cost of Cradle of Lifes budget alone. Figures for the Tomb Raider movies come from the weekend box office websites, box office mojo and box office guru. Hitman Hitman Budget $23,000,000 Gross Revenue $99,965,792 Hitman opened in 2,458 theaters in the United States and Canada, grossing $13,180,769 in its opening weekend, ranking fourth at the box office. As of July 2, 2008, the film has grossed $39,687,694 (Box Office Mojo (n, d) in the United States and Canada and $60,245,563 in other territories for a worldwide total of $99,933,257. The films DVD sales equal $27,858,148 in the US alone, putting the total profits for Hitman at around $128 million. The figures established from the box office data, on the website the-numbers.com 4.2 Film to Game Avatar James Camerons Avatar videogame has not been as successful as the film; Ubisoft has sold over 1.3 million copies of the game worldwide across all platforms (GamerLive.TV 2010). According to Michael Pachter, video game analyst for webush securities, Ubisoft shipped 2.5 million copies of the game at launch. He also believes that with Blu-ray and DVD releases of the movie there could be another push for the game at retail. But for the most part, the game has sold what its going to sell. He said the poor reviews that Ubisofts game received hurt hardcore gamer sales. 5 Receptions The reception of films and video games has always been vital, as reviews can determine whether a film or game can be successful. I looked at reviews and I also did some user tests, to see if they get involved or not with cross-media so they can try to feel every part of that universe. 5.1 Reviews I shall look at reviews of the two Tomb Raider Films, Hitman film and Avatar: The Game. Tomb Raider (Films) Lara Croft: Tomb Raider The film received generally negative reviews, with rotten tomatoes giving it an approval rating of 19%, with 27 out of 140 critics giving it a positive review all with an average rating of 3.9/10 (Rotten Tomatoes (n, d)). The general consensus is Angelina Jolie is perfect for the role of Lara Croft, but even she cant save the movie from a senseless plot and action sequences with no emotional impact. An unlikely positive review came from Roger Ebert who awarded the film three out of four stars and said, Lara Croft Tomb Raider elevates goofiness to an art form. Here is a movie so monumentally silly, yet so wondrous to look at, that only a churl could find fault. It was controversial for its many objectionable plots like an evil thing in a Hindu temple and breaking statues of a World heritage site. Cradle of Life Cradle of Life received slightly higher reviews than the original, with a 23% rating on Rotten Tomatoes based on 151 reviews (Rotten Tomatoes (n, d)) and a 43/100 rating on Metacritic (Metacritic (n, d)). It was described by Salon as a highly enjoyable summer thrill ride (Salon 2003). Roger Ebert gave the film 3 out of four stars, stating that the film was better than the first one, more assured, more entertaining..it uses imagination and exciting locations to give the movie the same kind of pulp adventure feeling we get from the Indiana Jones movies (Roger Ebert 2003). Cradle of Life was as heavily panned as its predecessor. Wesley Morris of the Boston Globe wrote, Its a bullet-riddled National Geographic special that produces a series of dumb, dismal shootouts that are so woefully choreographed theres reason to believe Debbie Allen may be behind them (boston.com (n, d)). Hitman (Film) The Hitman film has been almost universally panned by critics. The most common complaints are a lacking, often confusing plot, dry acting and extreme violence. On the film review aggregate website Rotten Tomatoes, Hitman has received a rating of 14% based on 98 reviews (Rotten Tomatoes (n, d)). However, film critic Roger Ebert gave it three stars out of four, saying Hitman stands right on the threshold between video games and art. On the wrong side of the threshold, but still, give it credit. On the Metacritic website, the film has received a Metascore of 35 out of 100 based on 22 reviews (Metacritic (n, d)). In 2009, Time listed the film on their list of top ten worst video games movies (Time (n, d)). Avatar (Video Game) Avatar: The Game has received mixed reception. It has been criticized for the games linear gameplay and said its controls are unintuitive and camera sloppy. The Wii version received mediocre scores mostly, having poor camera angling, frame rate and storytelling, but visuals and controls were spoken well of. The PC version garnered a Metascore of 65 on Metacritic (Metacritic (n, d)). On IGN, the game received a score of 5.9 for Wii, and 6.8 for the other consoles (IGN (n, d)). It got a 5.5 on Gamespot for the PC, PS3 and Xbox 360 versions, while it got a 4.0 for the PSP edition (Gamespot (n, d)). In contrast, according to Meant to be Seen (MTBS), the PC version fared much better with a rating of 8.2 out of 10. Its rationale is PC stereoscopic 3D displays are readily available. The reviewer of MTBS said In a year or two when S-3D gaming is much more common, I really think Avatar: The Game will be a regularly cited example that demonstrates how things should be done and what the artistic potential is behind good 3D gaming (MTBS 2009). Also, Gamesradar.com gave the Wii version a score of 7 out of 10, saying, Youll love that the forest world is lush and nicely realized, fun stealth-focused combat, and an overall a surprisingly decent movie tie-in. However, the site also said that, Youll hate heaps of ugly clipping; control frustrations hamper its stealthy nature, and its repetitive goals (Gamesradar 2009) Indiana Jones (Video Game) Indiana Jones and the Staff of Kings has received average reviews from the critics, Metacritic gave it an average of 55% (Metacritic (n, d)) The most universal criticism of the Wii version is the poorly implemented and only partially responsive motion controls. IGN gave it a 5.0/10 praising its interface, graphic effects, amount of extras, interactive levels, and varied gameplay, but criticizing its stupidly implemented motion controls(IGN 2009). The Origin (A.V. Club) gave it an F, a 0 on the Metacritic scale, calling the motion controls inexcusable and stating the games best aspect was the inclusion of the point-and-click adventure Indiana Jones and the Fate of Atlantis, further adding it surpasses Staff of Kings in everything, but the Gamecube-like graphics. Gamespot gave it a 3.5/10, criticizing its terribly laid-out checkpoints, out-of-date visuals and atrocious, annoying motion controls (AV Club 2009). 5.2 User Test Next is my User test, I will first outline the test, then analyse the questionnaire. 5.2.1 Outline of test The test consists of a list of questions which will be about which films, games, TV shows, and webisodes, people have seen and if they have seen or played them, because they have seen or played the original game, film, TV show and webisodes, example if you have seen the Avatar film have you played the game. I will try to get 20 people to fill out the questionnaire, and I will then analyse the results. Appendix: 5.2.2 Analysis of Questionnaire After completing my testing, this was the list of my results from the questionnaire that were filled out. I list them in a table with a 1 representing you had seen or played the media, and a 0 representing that you havent. I made the table more visual by highlighting the majority people who had watched or played both types of media from the same franchise. I highlighted the people who had watched the most (more or equal to 7), in terms of 2 of the same franchise, and I also highlighted the franchises which had been seen or played the most (more or equal to 7) this gave me a cross section of which is cross-media works best and which age group it appeals to more. After completing the testing and analyses you can see that majority of the results point at TV shows who expand their franchise, and that it is mainly 22-24 year olds who who follow the franchise. The trend in the age range seems to be if they like a show, they will follow it in different media. I will compare this to reviews to see if it is coincidence or if the reviews are generally responsible for this kind of response. 5.3 Compare Reviews and User Test Comparing the reviews from section 5.1 I looked at with table of results I created I have come to the conclusion, that if the review is bad then more often than not the audience wont go to watch or play that medium. The only exception I came across was Tomb Raider, in which both film reviews were poor, an average of 3.1/10, it still did increasingly well at the box office. There is even talk o

Implementation Of Clustering Algorithm K Mean K Medoid Computer Science Essay

Implementation Of Clustering Algorithm K Mean K Medoid Computer Science Essay Data Mining is a fairly recent and contemporary topic in computing. However, Data Mining applies many older computational techniques from statistics, machine learning and pattern recognition. This paper explores two most popular clustering techniques are the k-means k-medoids clustering algorithm. However, k-means algorithm is cluster or to group your objects based on attributes into K number of group and  k-medoids  is a  related to the  K-means  algorithm. These algorithms are based on the k partition algorithms and both attempt to minimize  squared error. In contrast to the K-means algorithm K-medoids chooses data points as centres. The algorithms have been developed in Java, for integration with Weka Machine Learning Software. The algorithms have been run with two dataset Facial palsy and Stemming. It is having been shown that the algorithm is generally faster and more accurate than other clustering algorithms. Data Mining derives its name from the similarities between searching for valuable business information in a large database (for example, finding linked products in gigabytes of store scanner data) and mining a mountain for a vein of valuable ore.[1] Both process requires either sifting through an immense amount of material. Or intelligently probing it to find exactly where the value resides. Data Mining Data mining is also known as knowledge mining. Before it was named DATA MINING, it was called data collection, data warehousing or data access. Data mining tools predicts the behaviours of the models that are loaded in the data mining tools (like Weka) for analysis, allowing making predicted analysis, of the model. Data mining provides hands-on and practical information. Data mining is the most powerful tool available now. Data mining can be used for modelling in fields such as artificial intelligence, and neural network. What does it do? Data mining take the data which exists in unrelated patterns and designs, and uses this data to predict information which can be compared in terms of statistical and graphical results. Data mining distil / filters the information from the data that is inputted and final model is generated. Clustering What is cluster analysis? Unlike classification and prediction, which analyse class-labeled data objects, clustering analyses data objects without consulting a known class label. A 2-D plot of customer data with respect to customer locations in a city, showing three data clusters. Each cluster center is marked with a +.[6] Clustering is the technique by which like objects are grouped together. The objects are clustered or grouped based on the principle of maximizing the intra class similarity and minimizing the interclass similarity. i.e. clusters of the objects are made so that the clusters have resemblance in comparison to one another, but are very divergent to objects in other clusters. Each cluster that is made can be viewed as a class of objects, from which rules can be derived. [6] Problem overview The problem at hand is able to correctly cluster a facial palsy dataset which is given by our lecturer. This section will provide an overview of dataset being analysed, and description about dataset that we use in this implementation. Data Set 1.3.1.1 Facial_Palsy_svmlight_format Facial Palsy data is for binary classification. +1 severe facial palsy faces -1 Non-severe or normal faces 66 Principal components generated from 5050 Hamming distance images 1.3.1.2 A6_df2_stemming__svm: Attributes: 100 A6_df2_stemming__svm_100.dat +1 Open question -1 Closed question Section 2 Methodology This section will firstly discuss the methodology behind K-means k-medoids algorithm. It is than followed by steps to implement k-means and k medoids algorithms. How many input, output and what are the steps to perform k-means and k-medoids. 2.1 K-mean K-means clustering starts with a single cluster in the centre, as the mean of the data. Here after the cluster is split into 2 clusters and the mean of the new cluster are iteratively trained. Again these clusters are split and the process goes on until the specified numbers of the cluster are obtained. If the specified number of cluster is not a power of two, then the nearest power of two above the number specified is selected and then the least important clusters are removed and the remaining clusters are again iteratively trained to get the final clusters. If the user specifies the random start, random cluster is generated by the algorithm, and it goes ahead by fitting the data points into these clusters. This process is repeated many times in loops, for as many random numbers the user chooses or specifies and the best value is found at the end. The output values are displayed. The drawbacks of the clustering method are that, the measurement of the errors or the uncertainty is ignored associated with the data. Algorithm: The k-means algorithm for partitioning, where each clusters centre is represented by the mean value of the objects in the cluster. Input: k: the number of clusters, D: a data set containing n objects. Output: A set of k clusters. Method: (1) Arbitrarily choose k objects from D as the initial cluster centers; (2) Repeat (3) reassign each object to the cluster to which the object is the most similar, based on the mean value of the objects in the cluster; (4) Update the cluster means, i.e., calculate the mean value of the objects for each cluster; (5) Until no change; Where E is the sum of the square error for all objects in the data set; p is the point in space representing a given object; and mi is the mean of cluster Ci (both p and mi are multidimensional). In other words, for each object in each cluster, the distance from the object to its cluster center is squared, and the distances are summed. This criterion tries to make the resulting k clusters as compact and as separate as possible.[2] Clustering of a set of objects based on the k-means method. (The mean of each cluster is marked by a +.) 2.2 K- Medoids This report recommends a new algorithm for K-medoids, which runs like the K-means algorithm. The algorithm proposed scans and calculates distance matrix, and use it for finding new medoids at every constant and repetitive step. The evaluation is based on real and artificial data and is compared with the results of the other algorithms. Here we are discussing the approach on k- medoids clustering, using the k-medoids algorithm. The algorithm is to be implemented on the dataset which consist of uncertain data. K-medoids are implemented because they to represent the centrally located objects called medoids in a cluster. Here the k-medoids algorithm is used to find the representative objects called the  medoids  in the dataset. Algorithm: k-medoids. PAM, a k-medoids algorithm for partitioning based on medoids or central objects. Input: k: the number of clusters, D: a data set containing n objects. Output: A set of k clusters. Method: (1) Arbitrarily choose k objects in D as the initial representative objects or seeds; (2) Repeat (3) Assign each remaining object to the cluster with the nearest representative object; (4) Randomly select a no representative object, o random; (5) Compute the total cost, S, of swapping representative object, oj, with o random; (6) If S (7) Until no change; Where E is the sum of the absolute error for all objects in the data set; p is the point in space representing a given object in cluster Cj; and oj is the representative object of Cj. In general, the algorithm iterates until, eventually, each representative object is actually the medoids, or most centrally located object, of its cluster. This is the basis of the k-medoids method for grouping n objects into k clusters.[6] 2.3 Distance Matrix An important step in most clustering is to select a  distance measure, which will determine how the  similarity  of two elements is calculated. Common distance metrics: Euclidean Manhattan Minkowski Hamming etcà ¢Ã¢â€š ¬Ã‚ ¦ Here in our implementation we choose two distance matrix that you can see below with description. 2.3.1 Euclidean Distance Metric The  Euclidean distance  between points  p  and  q  is the length of the  line segment. In  Cartesian coordinates, if  p  =  (p1,  p2  pn) and q  =  (q1,  q2  qn) are two points in  Euclidean  n-space, then the distance from  p  to  q  is given by: 2.3.2 Manhattan Distance Metric The Manhattan (or taxicab) distance,  d1, between two vectors  in an n-dimensional  real  vector space  with fixed  Cartesian coordinate system, is the sum of the lengths of the projections of the  line segment  between the points onto the  coordinate axes. Section 3 Discussion In this section we are discussing about how Weka Machine learning work and how we implemented both k-means and k medoids algorithm. To implement these two algorithms we use Java and we are explaining how we implemented in java which function we use in order to implement these two algorithms. 3.1 Weka Machine Learning Weka is a machine learning software made using Java and many other languages. Weka has a collection of tools that are used to analyse the data that the user inputs in the form of dataset files. Weka supports more than four different input data formats. Weka uses an interactive GUI interface, which is easy for the user to use.  Ã‚  Weka provides the functionality for testing and visual aid options that can be used by the user to compare and sort the results. 3.2 Implementation In this section, we discuss about implementation of 2 clustering algorithms: K-Means and K-Medoids. Here, we use Object Oriented Programming to implement these 2 algorithms. The structure of program as below: There are 3 packages: K-Mean, K-Medoid, main. Files in K-Mean package: Centroid.java Cluster.java KMean_Algorithm.java KMean_Test.java KMean_UnitTest.java Files in K-Medoid package: KMedoid_Algorithm.java KMedoid_UnitTest.java Files in main package: Attribute.java DataPoint.java DistanceCalculation.java FileFilter.java MainFrame.java Utilities.jav There are some main functions implemented for clustering activity as below: 3.2.1 read_SVMLightFile_fill_up_missing_attribute() This function is about reading the SVM Light data file (.dat) and fill up all the missing attributes/values in data file before returning a Vector of data-points for clustering activity. 3.2.2 calculate_distance() This function is providing calculation according to the distance metric input in order to calculate distance between data objects for clustering activity. Overall, this function provides calculation for 3 different distance metrics as: Euclidean, Manhattan and Minkowski. 3.2.3 startClustering() This function is about running a particular clustering algorithm and returns a Vector of Clusters with their own data-points inside. All the steps of a particular clustering algorithm is implemented, here we implement K_Means and K_Medoids clustering algorithms. 3.2.4 calculateSumOfSquareError() This function is about calculating the total/sum square error for all the output clusters. By calling the function calculateSquareError() inside every cluster and sum up, the sum of Square Error will be calculated as long as the clustering activity finished. 3.2.5 calculateSumOfAbsoluteError() This function is about calculating the total/sum absolute error for all the output clusters. By calling the function calculateAbsoluteError() inside every cluster and sum up, the sum of Absolute Error will be calculated as long as the clustering activity finished. 3.2.6 toString() and main() The toString() function will return a string which represents the clustering output, including: total objects of every cluster, percent of object in every cluster, the error (such as: sum of square error or sum of absolute error), the centroid of every cluster and all the data-points clustered in the clusters. The main() function inside MainFrame.java class will allow to execute the GUI of the program, so users can interact with system by GUI instead of console or command-line. In this GUI, users can choose type of distance metric (such as Euclidean and Manhattan), Clustering algorithm (such as K-Means and K-Medoids) and enter input parameters such as number of clusters and number of iterations for clustering activity. Besides, users also can open any data file to view or modify and save before running clustering as well as export the original data file with missing attributes/values to new processed data file with all missing values filled up by zero (0). Section 4 Analysis In order to access the performance of the K-means k-medoids clusters, two dataset of analyses was carried out. The aim of this set to tests was provide an indicator as to how well the clusters performed using the k-means and k-medoids function. The tests were involved comparing the cluster to other cluster of various types provided within Weka cluster suite. The results are summarised throughout the remainder of this section. 4.1 Experiment (Facial Palsy dataset) results vs. Weka Here In this section how we did a comparison with our application algorithm vs. Weka you can see below. In this pattern we give iterations when we run a dataset with our application and Weka. Iterations: 10 >> 30 >> 50 >> 100 >> 200 >> 300 >> 400 >> 500 In this pattern we give a cluster when we run a dataset with our application and Weka. Clusters: 2 >> 3 >> 4 >> 5 After we run dataset with this format than each and every run we get result we combine that result, compare with Weka, we make a total of each and every column and come with average and we are displaying in table that you can see in below table. This Symbol is object. To see a result please click on this object it will show you result. We put as object because result is too big in size so we are not able to put in this A4 page. 4.2 Experiment (Stemming Question dataset) results vs. Weka Here In this section how we did a comparison with our application algorithm vs. Weka you can see below. In this pattern we give iterations when we run a dataset with our application and Weka. Iterations: 10 >> 30 >> 50 >> 100 >> 200 >> 300 >> 400 >> 500 In this pattern we give a cluster when we run a dataset with our application and Weka. Clusters: 2 >> 3 >> 4 >> 5 After we run dataset with this format than each and every run we get result we combine that result, compare with Weka, we make a total of each and every column and come with average and we are displaying in table that you can see in below table. This Symbol is object. To see a result please click on this object it will show you result. We put as object because result is too big in size so we are not able to put in this A4 page. Section 5 Conclusion In evaluating the performance of data mining techniques, in addition to predicative accuracy, some researchers have been done the importance of the explanatory nature of models and the need to reveal patterns that are valid, novel, useful and may be most importantly understandable and explainable. The K-means and k-medoids clusters achieved this by successfully clustering with facial palsy dataset. Which method is more robust-k-means or k-medoids? The k-medoids method is more robust than k-means in the presence of noise and outliers, because a medoids is less influenced by outliers or other extreme values than a mean. However, its processing is more costly than the k-means method. Both methods require the user to specify k, the number of clusters. Aside from using the mean or the medoids as a measure of cluster center, other alternative measures are also commonly used in partitioning clustering methods. The median can be used, resulting in the k-median method, where the median or middle value is taken for each ordered attribute. Alternatively, in the k-modes method, the most frequent value for each attribute is used. 5.1 Future Work The K-means algorithm can create some in efficiency as; it scans the dataset leaving some noise and outliners. These small flaws can be considered major to some of the users, but this doesnt means that the implementation can be prevented. It is always possible that sometimes the dataset is more efficient to follow other algorithms more efficiently, and the result distribution can be equal or acceptable. It is always advisable to make the dataset more efficient by removing unwanted attributes and more meaning full by pre-processing the nominal values to the numeric values. 5.2 Summery Throughout this report the k-mean and the k-medoids algorithms are implemented, which find the best result by scanning the dataset and creating clusters. The algorithm was developed using Java API and more Java classes.  

American Idol Essay -- Cheathouse Essays

American Idol â€Å"That rendition was impressive, but you do not look like an American Idol; however, we will give you one more chance. Welcome to Hollywood!† A person with a mediocre voice that is not stunningly attractive would have a harder time advancing to Hollywood on American Idol. As the judges say, an American Idol should be a distinct person with an exceptionally fantastic voice. Culture is the training or refining of intellectual faculties and the way of life for a particular people. My goal will be to critique the ideal of image on American Idol and discuss how people may or may not get though based on looks alone. In the entertainment industry, images of the â€Å"perfect† people are portrayed as women being skinny, tan, and firm and men being handsome, muscular, and original. Judges Randy, Paula, and Simon critique contestants on American Idol mostly based on their image. Randy Jackson, Paula Abdul, and best-known Simon Cowell are the main judges on American Idol. They critique auditions in seven different cities where each performer sings and if well enough, receives the yellow ticket to Hollywood. However, â€Å"This show is called American Idol. It wasn’t good enough† is what many contestants hear. From the millions of people who try out, only a small percentage get though to the second round. The judges place those contestants into groups of three to perform on stage in front of a large audience. They eliminate hundreds of contestants to narrow the field down to jus...

New York State Accounting Code of Ethics Essay -- essays research pape

New York State Accounting Code of Ethics   Ã‚  Ã‚  Ã‚  Ã‚  The accounting system is constantly changing. During these changes, it is important for accountants to adhere to the high ethical standards that they have always lived by. Adhering to the high ethical standards is an accountant's obligation to the public, the profession, and themselves. An accountant's ethical conduct usually lies within four different areas. This includes competence, confidentiality, integrity, and objectivity. NYSSCPA.ORG states, "Members also have a continuing responsibility to cooperate with each other to improve the art of accounting, maintain the public's confidence, and carry out the professions special responsibilities for self-governance," (Article 1).   Ã‚  Ã‚  Ã‚  Ã‚  New York State expects its accountants to act in a way that will serve the public interest. The public includes clients, credit grantors, governments, employers, investors, the business and financial community, and any other person that relies on the information provided by the accountant. It is the accountant's responsibility to maintain an appropriate level of professional competence through continuing education of their knowledge and skills. New York State also expects its accountants to perform their duties in accordance with relevant laws and regulations, as well as providing clear and complete reports.   Ã‚  Ã‚  Ã‚  Ã‚  It is important for accountants to maintain their integrity. Often times, accountants are faced with situations that are questionable. ...